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kaushalshetty
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#### Description

Feature Selection using Genetic Algorithm (DEAP Framework)

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# FeatureSelectionGA

### Feature Selection using Genetic Algorithm (DEAP Framework)

Data scientists find it really difficult to choose the right features to get maximum accuracy especially if you are dealing with a lot of features. There are currenlty lots of ways to select the right features. But we will have to struggle if the feature space is really big. Genetic algorithm is one solution which searches for one of the best feature set from other features in order to attain a high accuracy.

$pip install feature-selection-ga  #### Documentation: https://featureselectionga.readthedocs.io/en/latest/ #### Usage: from sklearn.datasets import make_classification from sklearn import linear_model from feature_selection_ga import FeatureSelectionGA, FitnessFunction X, y = make_classification(n_samples=100, n_features=15, n_classes=3, n_informative=4, n_redundant=1, n_repeated=2, random_state=1) model = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto') fsga = FeatureSelectionGA(model,X,y, ff_obj = FitnessFunction()) pop = fsga.generate(100) #print(pop)  #### Usage (Advanced): By default, the FeatureSelectionGA has its own fitness function class. We can also define our own FitnessFunction class. class FitnessFunction: def __init__(self,n_splits = 5,*args,**kwargs): """ Parameters ----------- n_splits :int, Number of splits for cv  verbose: 0 or 1 """ self.n_splits = n_splits def calculate_fitness(self,model,x,y): pass  With this, we can design our own fitness function by defining our calculate fitness! Consider the following example from Vieira, Mendoca, Sousa, et al. (2013) $f(X) = \alpha(1-P) + (1-\alpha) \left(1 - \dfrac{N_f}{N_t}\right)\$

Define the constructor init with needed parameters: alpha and N_t.

class FitnessFunction:
def __init__(self,n_total_features,n_splits = 5, alpha=0.01, *args,**kwargs):
"""
Parameters
-----------
n_total_features :int
Total number of features N_t.
n_splits :int, default = 5
Number of splits for cv
alpha :float, default = 0.01
Tradeoff between the classifier performance P and size of
feature subset N_f with respect to the total number of features
N_t.

        verbose: 0 or 1
"""
self.n_splits = n_splits
self.alpha = alpha
self.n_total_features = n_total_features


Next, we define the fitness function, the name has to be calculate_fitness:

    def calculate_fitness(self,model,x,y):
alpha = self.alpha
total_features = self.n_total_features

    cv_set = np.repeat(-1.,x.shape[0])
skf = StratifiedKFold(n_splits = self.n_splits)
for train_index,test_index in skf.split(x,y):
x_train,x_test = x[train_index],x[test_index]
y_train,y_test = y[train_index],y[test_index]
if x_train.shape[0] != y_train.shape[0]:
raise Exception()
model.fit(x_train,y_train)
predicted_y = model.predict(x_test)
cv_set[test_index] = predicted_y

P = accuracy_score(y, cv_set)
fitness = (alpha*(1.0 - P) + (1.0 - alpha)*(1.0 - (x.shape[1])/total_features))
return fitness


Example: You may also see

example2.py
X, y = make_classification(n_samples=100, n_features=15, n_classes=3,
n_informative=4, n_redundant=1, n_repeated=2,
random_state=1)

Define the model
model = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')
Define the fitness function object
ff = FitnessFunction(n_total_features= X.shape[1], n_splits=3, alpha=0.05)
fsga = FeatureSelectionGA(model,X,y, ff_obj = ff)
pop = fsga.generate(100)